Many robots include end effectors that enable the robots to grasp various objects and manipulate those objects. Manipulating an object may include, for example, picking up the object or otherwise moving the object without necessarily picking it up (e.g., rotating a door knob, pulling a lever, moving an object across a surface). For example, a robot may utilize a gripper end effector such as an “impactive” gripper or “ingressive” gripper (e.g., physically penetrating an object using pins, needles, etc.) to pick up an object from a first location, move the object to a second location, and drop off the object at the second location. Some additional examples of robot end effectors that may grasp objects include “astrictive” end effectors (e.g., using suction or vacuum to pick up an object) and one or more “contigutive” end effectors (e.g., using surface tension, freezing or adhesive to pick up an object), to name just a few.
While humans innately know how to correctly grasp many different objects, determining an appropriate location to grasp an object for manipulation of that object may be a difficult task for robots. For example, some robots may rely on applying a color image of an object captured by a camera of the robot to a convolutional neural network that has been trained using color images labeled with valid grasps to determine a grasp for the object. However, the labeled images utilized to train the convolutional neural network are typically hand labeled, which may be a time-consuming task and/or may not encompass many objects that may be encountered by robots. Moreover, such approaches may be computationally slow and/or inaccurate. Also, for example, some robots may rely on applying a “brute force” approach to a 3D model of an object to determine a grasp for the object. However, such approaches may be computationally slow and/or inaccurate. Additional and/or alternative drawbacks of the aforementioned techniques and/or other techniques may be presented.